activity
20242026
most citedAuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach

3 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models

Sajjad Ghiasvand, Maryam Amirizaniani, Haniyeh Ehsani Oskouie +2

Open-ended aesthetic critique is a challenge for multimodal large language models (MLLMs): it has no single correct answer, and most aesthetic evaluation measures models against nu…

cs.CL2026

Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering

Maryam Amirizaniani, Benjamin Charles Germain Lee, Jevin West +1

Effective personalized question answering (PQA) in language models requires grounding responses in the user's underlying intent, where intent refers to the implicit ``why'' behind…

cs.CL2026

Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering

Maryam Amirizaniani, Alireza Salemi, Hamed Zamani

Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context. Existing state-of-the-ar…

cs.CL2025

IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering

Jieyong Kim, Maryam Amirizaniani, Soojin Yoon +1

Intent identification serves as the foundation for generating appropriate responses in personalized question answering (PQA). However, existing benchmarks evaluate only response qu…

cs.CL2024★ 2 cited

Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses

Maryam Amirizaniani, Elias Martin, Maryna Sivachenko +2

Theory of Mind (ToM) reasoning entails recognizing that other individuals possess their own intentions, emotions, and thoughts, which is vital for guiding one's own thought process…

cs.AI2024★ 3 cited

LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop

Maryam Amirizaniani, Jihan Yao, Adrian Lavergne +4

As Large Language Models (LLMs) become more pervasive across various users and scenarios, identifying potential issues when using these models becomes essential. Examples of such i…